Social media comprises any medium, network, channel, or technology for facilitating communication between a large number of individuals and/or entities (users). Some common examples of social media are Facebook® or Twitter®, each of which facilitates communications in a variety of forms between large numbers of users (Facebook is a trademark of Facebook, Inc. in the United States and in other countries. Twitter is a trademark of Twitter Inc. in the United States and in other countries.) Social media, such as Facebook or Twitter allow users to interact with one another individually, in a group, according to common interests, casually or in response to an event or occurrence, and generally for any reason or no reason at all.
Some other examples of social media are websites or data sources associated with radio stations, news channels, magazines, publications, blogs, and sources or disseminators of news or information. Some more examples of social media are websites or repositories associated with specific industries, interest groups, action groups, committees, organizations, teams, or other associations of users.
Data from social media comprises unidirectional messages, or bi-directional or broadcast communications in a variety of languages and forms. Such communications in the social media data can include proprietary conversational styles, slangs or acronyms, urban phrases in a given context, formalized writing or publication, and other structured or unstructured data.
A user's contributions or interactions with the social media can include any type or size of data. For example, a user can post text, pictures, videos, links, or combinations of these and other forms of information to a social media website. Furthermore, such information can be posted in any order, at any time, for any reason, and with or without any context. Thus, a user's interactions with a social media can be regarded as unstructured data.
For example, one user—a posting user—posts some content on social media. Another user—a reacting user—reacts or interacts with that post of the posting user. For example, the reacting user may indicate a liking or dislike of the post, may comment on the post, may share that post with others, and perform other such interactions.
Hereinafter, a user who posts content on social media is referred to as a posting user. Content posted by a posting user is referred to as original content. Original content can be edited or changed by the posting user. A changed form of an original content of a post is referred to as edited content. A reacting user is a user different from the posting user, and reacts to or interacts with a post of a posting user. A reaction or interaction of a reacting user with a post of a posting user is collectively and interchangeably referred to as a reaction.
A reaction of a reacting user is a suitable manifestation of an agreement or disagreement with the content of the post. For example, while liking or disliking a post is a manifestation of an agreement or disagreement, respectively, of the reacting user with the post, simply viewing or playing the content of the post is not a manifestation of an agreement or disagreement. Similarly, commenting on a post is a manifestation of an agreement or disagreement depending upon the sentiment of the comment. Sharing a post is also a manifestation of an agreement or disagreement depending upon the sentiment of the commentary contributed by the reacting user with the shared post.
The content of posts, some reactions, or a combination thereof can be expressed in a natural language. Natural language is written or spoken language having a form that is employed by humans for primarily communicating with other humans or with systems having a natural language interface.
Natural language processing (NLP) is a technique that facilitates exchange of information between humans and data processing systems. For example, one branch of NLP pertains to transforming human readable or human understandable content into machine usable data. For example, NLP engines are presently usable to accept input content such as a social media post or human speech, and produce structured data—such as an outline of the input content, most significant and least significant parts, a subject, a reference, dependencies within the content, and the like, from the given content.
Shallow parsing is a term used to describe lexical parsing of a given content using NLP. For example, given a sentence, an NLP engine determining what the sentence semantically means (context) according to the grammar of the language of the sentence is the process of lexical parsing, to wit, shallow parsing. In contrast, deep parsing is a process of recognizing the relationships, predicates, or dependencies, and thereby extracting new, hidden, indirect, or detailed structural information from distant content portions in a given document or some corpora.
A sentiment of a given content can be determined using NLP. For example, by performing NLP on the content of a post, it can be determined whether the content expresses a favorable or unfavorable sentiment about a subject. As an example, “I like strawberries” post can be parsed using NLP to determine that the post has a favorable sentiment towards strawberries, whereas “I hate strawberries” post can be parsed using NLP to determine that the post has an unfavorable sentiment towards strawberries.